Reducing building energy demand is a crucial part ofthe global response to climate change, and evolutionary
algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool
for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive:
optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate
fitness models are a possible solution to this problem, but few approaches have been demonstrated
for multi-objective, constrained or discrete problems, typical of the optimisation problems in building
design. This paper presents a modified version of a surrogate based on radial basis function networks,
combined with a deterministic scheme to deal with approximation error in the constraints by allowing
some infeasible solutions in the population. Different combinations of these are integrated with NonDominated
Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building
optimisation problem. The comparisons show that the surrogate and constraint handling combined offer
improved run-time and final solution quality. The paper concludes with detailed investigations of the
constraint handling and fitness landscape to explain differences in performance.

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This is an open access article published by Elsevier under the CC BY license (http://creativecommons.org/licenses/by/4.0/).